# Analytics Gallery with tidycensuskr

#### December 30, 2025

## Example 1: Spatial autocorrelation of economic activity in South Korea

### Design idea

In this example, we use Moran’s I statistics to analyze the spatial distribution of economic activity (measured by the number of registered companies) across South Korea in 2020.

- Global Moran’s I evaluates whether there is overall spatial autocorrelation, i.e., whether areas with similar company counts tend to cluster together nationwide.
- Local Moran’s I (LISA) identifies local clusters and outliers by classifying each district into four types:
  - High-High (H-H): districts with high values surrounded by other high-value districts.
  - Low-Low (L-L): districts with low values surrounded by other low-value districts.
  - High-Low (H-L): potential spatial outliers with high values surrounded by low-value neighbors.
  - Low-High (L-H): potential spatial outliers with low values surrounded by high-value neighbors.

The resulting LISA cluster map highlights where economic activities are concentrated and where they are sparse relative to neighboring areas.

### Data prep

    # Load 2020 boundaries
    data(adm2_sf_2020)

    # Load 2020 economy data
    df_2020_economy <- anycensus(year = 2020,
                                 type = "economy")

    # Merge with spatial data
    adm2_sf_2020_economy <- adm2_sf_2020 |>
      dplyr::inner_join(df_2020_economy, by = "adm2_code")

    # Variable of interest: number of companies
    var <- adm2_sf_2020_economy$company_total_cnt

### Global Moran’s I

    # Build neighbors (queen contiguity) and spatial weights
    nb <- poly2nb(adm2_sf_2020_economy, queen = TRUE)
    lw <- nb2listw(nb, style = "W", zero.policy = TRUE)

    # Global Moran's I test
    global_moran <- moran.test(var, lw, zero.policy = TRUE)
    global_moran

    ##
    ##  Moran I test under randomisation
    ##
    ## data:  var
    ## weights: lw
    ## n reduced by no-neighbour observations
    ##
    ## Moran I statistic standard deviate = 10.35, p-value < 2.2e-16
    ## alternative hypothesis: greater
    ## sample estimates:
    ## Moran I statistic       Expectation          Variance
    ##       0.424571643      -0.004149378       0.001715960

### Local Moran’s I and LISA map

    # Local Moran's I
    local_moran <- localmoran(var, lw, zero.policy = TRUE)

    # Bind results back to sf object
    adm2_sf_2020_economy <- adm2_sf_2020_economy |>
      mutate(
        Ii   = local_moran[, "Ii"],
        pval = local_moran[, "Pr(z != E(Ii))"]
      )

    mean_var <- mean(var, na.rm = TRUE)

    adm2_sf_2020_economy <- adm2_sf_2020_economy |>
      mutate(
        cluster = case_when(
          var > mean_var & Ii > 0 & pval <= 0.05 ~ "High-High",
          var < mean_var & Ii > 0 & pval <= 0.05 ~ "Low-Low",
          var > mean_var & Ii < 0 & pval <= 0.05 ~ "High-Low",
          var < mean_var & Ii < 0 & pval <= 0.05 ~ "Low-High",
          TRUE ~ "Not significant"
        )
      )

    ggplot(adm2_sf_2020_economy) +
      geom_sf(aes(fill = cluster), color = "grey70", size = 0.05) +
      scale_fill_manual(
        values = c(
          "High-High"     = "red",
          "Low-Low"       = "blue",
          "High-Low"      = "pink",
          "Low-High"      = "lightblue",
          "Not significant" = "white"
        )
      ) +
      labs(title = "LISA Cluster Map of Company units (2020)") +
      theme_minimal()

The results reveal a strong metropolitan concentration of economic activity. High-High (H-H) clusters are predominantly located in the Seoul Metropolitan Area, reflecting its central role in South Korea’s economy. Low-Low (L-L) clusters appear in Gangwon Province and along the borders of Jeollabuk-do, Jeollanam-do, Gyeongsangbuk-do, and Chungcheongnam-do, indicating regions with consistently low levels of company presence. This spatial pattern highlights the dominance of the capital region in economic activity and the relative sparsity of industrial and business units in peripheral provinces.

## Example 2: Population change by sex and districts

### Design idea

1. Load `censuskor` bundled in `tidycensuskr`, focusing on population counts by sex at the adm2 level
2. Clean and align administrative codes to account for district name changes, promotions, and boundary adjustments over time
3. Convert population counts into comparable units (thousands) and retain the most recent district names for consistent labeling
4. Prepare a `geofacet` grid based on 2020 administrative boundaries
5. Plot population trends over time separately for males and females using line charts

    # load packages
    library(geofacet)

    # load bundled data in tidycensuskr
    data(censuskor)
    data(adm2_sf_2020)
    data(kr_grid_adm2_sgis_2020)

    # prepare geofacet grid data
    # Use the newest adm2_code and name if one got its name changed or promoted
    pop <- censuskor |>
      dplyr::filter(
        type == "population" & class1 == "all households"
      ) |>
      dplyr::rename(code = adm2_code) |>
      dplyr::filter(class1 == "all households", class2 != "total") |>
      dplyr::mutate(
        value = value / 1000,
        code = dplyr::case_when(
          # Michuhol-gu (i.e., 23030 to 23090)
          code == 23030 ~ 23090,
          # Yeoju-si
          code == 31320 ~ 31280,
          # Dangjin-si
          code == 34390 ~ 34080,
          TRUE ~ code
        )
      ) |>
      dplyr::arrange(code, class2, -year) |>
      dplyr::group_by(code) |>
      dplyr::mutate(adm2 = adm2[which.max(year)]) |>
      dplyr::ungroup()

    head(pop)

    ## # A tibble: 6 × 10
    ##    year adm1  adm1_code adm2       code type       class1     class2 unit  value
    ##   <dbl> <chr>     <dbl> <chr>     <dbl> <chr>      <chr>      <chr>  <chr> <dbl>
    ## 1  2020 Seoul        11 Jongno-gu 11010 population all house… female pers…  72.6
    ## 2  2015 Seoul        11 Jongno-gu 11010 population all house… female pers…  73.8
    ## 3  2010 Seoul        11 Jongno-gu 11010 population all house… female pers…  76.5
    ## 4  2020 Seoul        11 Jongno-gu 11010 population all house… male   pers…  67.3
    ## 5  2015 Seoul        11 Jongno-gu 11010 population all house… male   pers…  69.5
    ## 6  2010 Seoul        11 Jongno-gu 11010 population all house… male   pers…  71.3

    # for a geofacet plot
    # map codes to district names for facet labels
    pop_name_map <- pop %>%
      dplyr::distinct(code, adm2) %>%
      { setNames(.$adm2, .$code) }

    pop_labels <- pop %>% dplyr::distinct(code, adm2)

    # Adjust for the identical district names in different provinces
    kr_grid_adm2_sgis_2020 <-
      kr_grid_adm2_sgis_2020 |>
      dplyr::mutate(
        name = dplyr::case_when(
          grepl("^32", code) & name == "Goseong-gun" ~ "Goseong-gun (GW)",
          grepl("^38", code) & name == "Goseong-gun" ~ "Goseong-gun (GN)",
          grepl("^11", code) & name == "Jung-gu" ~ "Jung-gu (SE)",
          grepl("^21", code) & name == "Jung-gu" ~ "Jung-gu (BU)",
          grepl("^22", code) & name == "Jung-gu" ~ "Jung-gu (DG)",
          grepl("^23", code) & name == "Jung-gu" ~ "Jung-gu (IC)",
          grepl("^25", code) & name == "Jung-gu" ~ "Jung-gu (DJ)",
          grepl("^26", code) & name == "Jung-gu" ~ "Jung-gu (UL)",
          grepl("^21", code) & name == "Seo-gu" ~ "Seo-gu (BU)",
          grepl("^22", code) & name == "Seo-gu" ~ "Seo-gu (DG)",
          grepl("^23", code) & name == "Seo-gu" ~ "Seo-gu (IC)",
          grepl("^24", code) & name == "Seo-gu" ~ "Seo-gu (GJ)",
          grepl("^25", code) & name == "Seo-gu" ~ "Seo-gu (DJ)",
          grepl("^26", code) & name == "Seo-gu" ~ "Seo-gu (UL)",
          grepl("^21", code) & name == "Nam-gu" ~ "Nam-gu (BU)",
          grepl("^22", code) & name == "Nam-gu" ~ "Nam-gu (DG)",
          grepl("^24", code) & name == "Nam-gu" ~ "Nam-gu (GJ)",
          grepl("^26", code) & name == "Nam-gu" ~ "Nam-gu (UL)",
          grepl("^21", code) & name == "Dong-gu" ~ "Dong-gu (BU)",
          grepl("^22", code) & name == "Dong-gu" ~ "Dong-gu (DG)",
          grepl("^23", code) & name == "Dong-gu" ~ "Dong-gu (IC)",
          grepl("^24", code) & name == "Dong-gu" ~ "Dong-gu (GJ)",
          grepl("^25", code) & name == "Dong-gu" ~ "Dong-gu (DJ)",
          grepl("^26", code) & name == "Dong-gu" ~ "Dong-gu (UL)",
          grepl("^21", code) & name == "Buk-gu" ~ "Buk-gu (BU)",
          grepl("^22", code) & name == "Buk-gu" ~ "Buk-gu (DG)",
          grepl("^24", code) & name == "Buk-gu" ~ "Buk-gu (GJ)",
          grepl("^26", code) & name == "Buk-gu" ~ "Buk-gu (UL)",
          grepl("^11", code) & name == "Gangseo-gu" ~ "Gangseo-gu (SE)",
          grepl("^21", code) & name == "Gangseo-gu" ~ "Gangseo-gu (BU)",
          TRUE ~ name
        )
      )

### Small multiples

This design highlights heterogeneous local population trajectories while preserving a sense of national spatial structure.

    ggplot(data = pop) +
      geom_line(
        aes(x = year, y = value, group = interaction(adm2, class2), color = class2),
        alpha = 0.5,
        linewidth = 1.5
      ) +
      facet_geo(~ code, grid = kr_grid_adm2_sgis_2020, label = "name", scale = "free_y") +
      labs(
        title = "Population Trends in South Korea by Sex and District",
        x = "Year",
        y = "",
        color = "Population Class",
        caption = "Y-axis values are not commensurate with the original scale"
      ) +
      scale_color_manual(values = c(female = "#F44336", male = "#2196F3")) +
      theme_void() +
      scale_x_continuous(
        breaks = sort(unique(pop$year)),
        labels = function(x) sprintf("%d", as.integer(x))
      ) +
      theme(
        strip.text = element_text(
          size = 5,
          margin = margin(0.05, 0.05, 0.05, 0.05, "cm")
        ),
        strip.background = element_blank(),
        axis.text.x = element_text(size = 6, angle = 90, hjust = 1),
        axis.text.y = element_blank(),
        panel.spacing = grid::unit(1, "pt"),
        plot.margin = margin(1, 1, 1, 1, "mm")
      )